We train five identical models on fully connected graphs of instances from TSP20, TSP50, TSP100, TSP200 and variable TSP20-50.
The line plots of optimality gap across TSP sizes in Figure REF  indicates that learning from variable TSP sizes helps models retain performance across the range of graph sizes seen during training (TSP20-50).
Variable graph training compared to training solely on the maximum sized instances (TSP50) leads to marginal gains on small instances but, somewhat counter-intuitively, does not enable better generalization to larger problems.
Learning from small TSP20 is unable to generalize to large sizes while TSP100 models generalize poorly to trivially easy sizes, suggesting that the prevalent protocol of evaluation on training sizes [1]}, [2]} overshadows brittle out-of-distribution performance.
